{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd  \n",
    "from matplotlib import pyplot as plt \n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>density</th>\n",
       "      <th>Sugar_content</th>\n",
       "      <th>label</th>\n",
       "      <th>intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.4600</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.3760</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.2640</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.3180</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.2370</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.481</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.2110</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.0910</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.0267</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  density  Sugar_content  label  intercept\n",
       "0    1    0.697         0.4600      1        1.0\n",
       "1    2    0.774         0.3760      1        1.0\n",
       "2    3    0.634         0.2640      1        1.0\n",
       "3    4    0.608         0.3180      1        1.0\n",
       "4    5    0.556         0.2150      1        1.0\n",
       "5    6    0.403         0.2370      1        1.0\n",
       "6    7    0.481         0.1490      1        1.0\n",
       "7    8    0.437         0.2110      1        1.0\n",
       "8    9    0.666         0.0910      0        1.0\n",
       "9   10    0.243         0.0267      0        1.0\n",
       "10  11    0.245         0.0570      0        1.0\n",
       "11  12    0.343         0.0990      0        1.0\n",
       "12  13    0.639         0.1610      0        1.0\n",
       "13  14    0.657         0.1980      0        1.0\n",
       "14  15    0.360         0.3700      0        1.0\n",
       "15  16    0.593         0.0420      0        1.0\n",
       "16  17    0.719         0.1030      0        1.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读入csv文件数据  \n",
    "df = pd.read_csv('../resource/watermelon3.0alpha.csv')\n",
    "df['intercept'] = 1.0  \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataMat = np.array(df[['density','Sugar_content','intercept']].values[:,:])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labelMat = np.mat(df['label'].values[:]).transpose()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sigmoid(z):  \n",
    "    return 1.0 / (1 + np.exp(-z))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def LogisticRegression(dataMat,labelMat):  \n",
    "    m,n = np.shape(dataMat)  \n",
    "    alpha = 0.05       # 步长\n",
    "    iterations = 2000  # 最大迭代次数\n",
    "    weights = np.array(np.array(np.ones((n,1))))  # 初始权重\n",
    "    \n",
    "    # 用梯度上升法更新权重\n",
    "    for k in range(iterations):    \n",
    "        z = np.dot(dataMat,weights)        \n",
    "        error = labelMat - sigmoid(z)            \n",
    "        weights = weights + alpha * np.dot(dataMat.transpose(),error)  \n",
    "        \n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def DrawResult(weights):  \n",
    "    m = np.shape(dataMat)[0]  \n",
    "    xcord1, ycord1, zcord1= [], [], []\n",
    "    xcord2, ycord2, zcord2= [], [], []\n",
    "    xcord3, ycord3, zcord3= [], [], []\n",
    "    xcord4, ycord4, zcord4= [], [], []\n",
    "    \n",
    "    # 按照类别划分数据点\n",
    "    for i in range(m):  \n",
    "        if labelMat[i]==1:  \n",
    "            z = sigmoid(dataMat[i,:] * weights)\n",
    "            if z>= 0.5:\n",
    "                xcord1.append(dataMat[i,0])  \n",
    "                ycord1.append(dataMat[i,1]) \n",
    "                zcord1.append(z)\n",
    "            else:\n",
    "                xcord2.append(dataMat[i,0])  \n",
    "                ycord2.append(dataMat[i,1]) \n",
    "                zcord2.append(z)\n",
    "        else:  \n",
    "            z = sigmoid(dataMat[i,:] * weights)\n",
    "            if z< 0.5:\n",
    "                xcord3.append(dataMat[i,0])  \n",
    "                ycord3.append(dataMat[i,1]) \n",
    "                zcord3.append(z)\n",
    "            else:\n",
    "                xcord4.append(dataMat[i,0])  \n",
    "                ycord4.append(dataMat[i,1]) \n",
    "                zcord4.append(z)\n",
    "    \n",
    "    # 创建三维图表\n",
    "    fig = plt.figure()  \n",
    "    ax = Axes3D(fig)\n",
    "    \n",
    "    # 画出判决平面\n",
    "    x = np.arange(0.2,1.0,0.1)\n",
    "    y = np.arange(0.0,0.6,0.1)\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    z = np.array([0.5 for x,y in zip(np.ravel(X), np.ravel(Y))])\n",
    "    Z = z.reshape(X.shape)    \n",
    "    ax.plot_surface(X, Y, Z, rstride=10, cstride=10, color=(0,0.6,0.6,0.1))\n",
    "    \n",
    "    # 按类别画出数据点\n",
    "    ax.scatter(xcord1,ycord1,zcord1,s=30,c='red') \n",
    "    ax.scatter(xcord2,ycord2,zcord2,s=30,c='red',marker='x')  \n",
    "    ax.scatter(xcord3,ycord3,zcord3,s=30,c='green') \n",
    "    ax.scatter(xcord4,ycord4,zcord4,s=30,c='green',marker='x')  \n",
    "    \n",
    "    ax.set_xlim(0.0,1.0)\n",
    "    ax.set_ylim(-0.1,0.7)\n",
    "    ax.set_zlim(-0.2,1.2)\n",
    "    ax.set_xlabel('density')  \n",
    "    ax.set_ylabel('Sugar content')\n",
    "    ax.set_zlabel('Logit')\n",
    "    plt.title('Gradient ascent logistic regression')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weights = LogisticRegression(dataMat,labelMat)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BXeDMZjPXTYAKnsVigcPhgEajES0YUrs6Q4UKtsPhgEqlglqthl6vD3tOkVys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\nsVmFAx9jjLFZ5b/60e/eqkf3wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2677123bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DrawResult(weights) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
